In short: PaW trains environment models during policy training using the same RL rollouts, consistently improving agent performance without requiring additional simulators or inference costs.
Reinforcement Learning (RL) teaches large language models which actions are rewarded, but tells them nothing about the environmental consequences of their actions. A new method called PaW leverages the RL data that already exists to simultaneously optimize an environment model without additional inference overhead.
Reinforcement Learning improves the performance of LLM agents through reward signals, but provides little information about how actions affect the environment. World Modeling could fill this gap, but typically requires separate simulators, additional training phases, or extra compute at inference time – all practical hurdles for production deployment.
The researchers argue that the data generated during RL rollouts already contains the signal needed: each transition couples an action with the resulting next observation. On this basis, they propose PaW – a framework that adds world-modeling supervision directly during policy training as an additional learning objective, without changing inference behavior.
To make this additional supervision informative and stable, PaW introduces three components: action-entropy-based selection of world-model training data, a noise-tolerant world-model loss, and adaptive loss weighting depending on rewards. Experiments on three agentic-task benchmarks show consistent improvements over pure RL baselines – across different models and RL algorithms.
The result is practically relevant: the RL rollouts that are generated anyway are a usable source for world-modeling training, without engineers needing to build additional infrastructure. This reduces implementation overhead and enables existing RL training pipelines to be extended with improved agent robustness.
Source: arxiv.org · Published May 31, 2026
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